The present invention relates to systems and methods for medical imaging. More particularly, the invention relates to systems and methods for reconstructing medical images.
Dynamic imaging, such as dynamic magnetic resonance imaging (“MRI”) or computed tomography (“CT”) is based on the repeated scanning of some anatomy to observe morphological, functional, or parametric changes. Simultaneously achieving high spatial, temporal, parametric, and contrast resolution is a main challenge faced in all dynamic imaging applications.
Contemporary dynamic imaging protocols combine undersampled acquisitions with reconstruction models that specifically exploit the highly redundant spatiotemporal structure of the dynamic imaging data. For example, periodic cardiac motion can be efficiently modeled in the temporal Fourier domain.
Recent works have suggested that high-quality dynamic MRI reconstructions can be achieved by promoting low-rank (“LR”) structure in the estimated series globally. With such techniques, aggressive rank reduction will result in temporal blurring, while only modest rank reduction will fail to adequately remove spatial blurring. Promoting low-rank structure during dynamic MRI series reconstruction has strong practical and theoretical appeal, but the disproportionality in size between the spatial and temporal, or parametric, dimensions of most dynamic MRI data series inhibits the practical utilization of the standard global approach.
Therefore, it would be desirable to have a system and method for reconstructing and processing dynamic or time-series images to provide greater image quality, such as having lower over all noise, without adversely reducing clinically-useful information within the images.